Fcsm June2012 Cooper Mell

Tackling Big Data

Michael Cooper & Peter Mell
NIST Information Technology Laboratory
Computer Security Division

IT Laboratory Big Data Working Group
• What exactly is Big Data?
• What are the issues associated with it?
• What role should NIST play with regard to Big Data?

• What is the relationship between Big Data and IT
Security?

What is Big Data?
• You know it when you see it …..
• NIST
– Astronomical Image data from ALMA ~1Tb / day
– Border Gateway Protocol (BGP) Data ~ 10 Tb

• Government
– Census
– NIH/ NCI

• Industry
– Amazon
– Google

What are the issues associated with Big Data?

•
•

Taxonomies, ontologies, schemas, workflow
Perspectives – backgrounds, use cases

•
•
•

Bits – raw data formats and storage methods
Cycles – algorithms and analysis
Screws – infrastructure to support Big Data

IT Security and Big Data
• Big data sources become rich targets
• Composition of data in one large source as well as
across sources
• Security data becoming the source for big data
repositories
– Log/event aggregation and correlation
– IDS/IPS databases

NIST ITL Big Data Planned Activities
• ITL/SSD Big Data Workshop – 13 – 14 June
• NIST Internal workshop this summer
• Government/ industry / academia conference this
fall

An Overview of Big Data
Technology and Security
Implications

Peter Mell
Senior Computer Scientist
NIST Information Technology Laboratory
http://twitter.com/petermmell

Disclaimer
The ideas herein represent the author’s notional views on
big data technology and do not necessarily represent the
official opinion of NIST.
Any mention of commercial and not-for-profit entities,
products, and technology is for informational purposes
only; it does not imply recommendation or endorsement
by NIST or usability for any specific purpose.

NIST Information Technology Laboratory

Presentation Outline
•
•
•
•
•
•
•

Section 1: Introduction and Definitions
Section 2: Big Data Taxonomies
Section 3: Security Implications and Areas of Research
Section 4: MapReduce and Hadoop
Section 5: Notable Implementations
Appendix A: Seminal Research Results
Appendix B: Overview of Big Data Framework Types

NIST Information Technology Laboratory

Section 1: Introduction and Definitions

NIST Information Technology Laboratory

Big Data – the Data Deluge
• The world is creating ever more data
– (and it’s a mainstream problem)

• Mankind created data
– 150 exabytes in 2005
• (exabyte is a billion gigabytes)
– 1200 exabytes in 2010
– 35000 exabytes in 2020 (expected by IBM)

• Examples:
–
–
–
–
–
–

U.S. drone aircraft sent back 24 years worth of video footage in 2009
Large Hadron Collider generates 40 terabytes/second
Bin Laden’s death: 5106 tweets/second
Around 30 billion RFID tags produced/year
Oil drilling platforms have 20k to 40k sensors
Our world has 1 billion transistors/human

Credit: The data deluge, Economist; Understanding Big Data, Eaton et al.

11

A Quick Primer on Data Sizes

12

Predictions of the “Industrial Revolution
of Data” – Tim O’Reilly
• Data is the new “raw material of
business” – Economist
• Challenges to achieving the
revolution
– It is not possible to store all the
data we produce
– 95% of created information was
unstructured in 2010

• Key observation
– Relational database management
systems (RDBMS) will be
challenged to scale up or out to
meet the demand
Credit: Data data everywhere, Economist; Extracting Value from Chaos, Gantz et al.

13

Industry Views on Big Data
• O’Reilly Radar definition:
– Big data is when the size of the data itself becomes part of
the problem

• EMC/IDC definition of big data:
– Big data technologies describe a new generation of technologies
and architectures, designed to economically extract value from
very large volumes of a wide variety of data, by enabling highvelocity capture, discovery, and/or analysis.

• IBM says that ”three characteristics define big data:”
– Volume (Terabytes -> Zettabytes)
– Variety (Structured -> Semi-structured -> Unstructured)
– Velocity (Batch -> Streaming Data)

• Microsoft researchers use the same tuple
Credit: Big Data Now, Current Perspectives from O’Reilly Radar (O’Reilly definition); Extracting Value from Chaos, Gantz et al. (IDC definition);
Understanding Big Data, Eaton et al. (IBM definition) ; The World According to LINQ, Meijer (Microsoft research)

14

Notional Definition for Big Data
• Big Data
– Big data is where the data volume, acquisition velocity, or
data representation limits the ability to perform effective
analysis using traditional relational approaches or requires
the use of significant horizontal scaling for efficient
processing.
Big Data

Big Data
Science

Big Data
Framework

Big Data
Infrastructure
15

More Notional Definitions
• Big Data Science

– Big data science is the study of techniques covering the acquisition,
conditioning, and evaluation of big data. These techniques are a
synthesis of both information technology and mathematical
approaches.

• Big Data Frameworks

– Big data frameworks are software libraries along with their
associated algorithms that enable distributed processing and
analysis of big data problems across clusters of compute units (e.g.,
servers, CPUs, or GPUs).

• Big Data Infrastructure

– Big data infrastructure is an instantiation of one or more big data
frameworks that includes management interfaces, actual servers
(physical or virtual), storage facilities, networking, and possibly
back-up systems. Big data infrastructure can be instantiated to
solve specific big data problems or to serve as a general purpose
analysis and processing engine.

16

Big Data Frameworks are often
associated with the term NoSQL
• NoSQL Origins

Structured
Storage

RDBMS

NoSQL

– First used in 1998 to mean “No to SQL”
– Reused in 2009 when it came to mean “Not Only SQL”
– Groups non-relational approaches under a single term

• The power of SQL is not needed in all problems
– Specialized solutions may be faster or more scalable
– NoSQL generally has less querying power than SQL

• Common reasons to use NoSQL
– Ability to handle semi-structured and unstructured data
– Horizontal scalability

• NoSQL may complement RDBMS (but sometimes
replaces)
– RDBMS may hold smaller amounts of high-value structured data
– NoSQL may hold vast amounts of less valued and less structured
data
Credit: NoSQL Databases, Strauch; Understanding Big Data, Eaton et al.

17

Common Tradeoffs Between Relational and NoSQL
Approaches
• Relational implementations provide ACID guarantees
–
–
–
–

Atomicity: transaction treated an all or nothing operation
Consistency: database values correct before and after
Isolation: events within transaction hidden from others
Durability: results will survive subsequent malfunction

• NoSQL often provides BASE
– Basically available: Allowance for parts of a system to fail
– Soft state: An object may have multiple simultaneous values
– Eventually consistent: Consistency achieved over time

• CAP Theorem
– It is impossible to have consistency, availability, and
partition tolerance in a distributed system
– the actual theorem is more complicated (see CAP slide in
appendix A)
Credit: Principles of Transaction-Oriented Database Recovery, Haerder and Reuter, 1983; Base: An ACID Alternative, Pritchett, 2008;
Brewer’s Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services, Gilbert and Lynch

18

CAP Theorem with ACID and BASE Visualized

ACID with
eventual availability

Partition
Tolerance

Consistency

BASE with
eventual consistency

Availability

Small data sets can be both
consistent and available
19

Section 2: Big Data Taxonomies

NIST Information Technology Laboratory

Big Data Characteristics and
Derivation of a Notional Taxonomy
Volume

Velocity

Variety
(semi-structured
or unstructured)

Requires
Horizontal
Scalability

Relational
Limitation

Big Data

No
No
No
No
Yes
Yes
Yes
Yes

No
No
Yes
Yes
No
No
Yes
Yes

No
Yes
No
Yes
No
Yes
No
Yes

No
No
Yes
Yes
Yes
Yes
Yes
Yes

No
Yes
Maybe
Yes
Maybe
Yes
Maybe
Yes

No
Yes, Type 1
Yes, Type 2
Yes, Type 3
Yes, Type 2
Yes, Type3
Yes, Type 2
Yes, Type 3

Types of Big Data:
Type 1: This is where a non-relational data representation required for effective analysis.
Type 2: This is where horizontal scalability is required for efficient processing.
Type 3: This is where a non-relational data representation processed with a horizontally
scalable solution is required for both effective analysis and efficient processing.
In other words, the data representation is not conducive to a relational algebraic analysis.

21

NoSQL Taxonomies
• Remember that big data frameworks and NoSQL are
related but not necessarily the same
– some big data problems may be solved relationally
•
•
•
•

Scofield: Key/value, column, document, graph
Cattel: Key/value, extensible record (e.g., column), document
Strauch: Key/value, column, document (mentions graph separately)
Others exist with very different categories

• Consensus taxonomy for NoSQL:
– Key/value, column, document, graph

• Notional big data framework taxonomy:
– Key/value, column, document, graph, sharded RDBMSs
Credit: NoSQL Databases, Strauch; NoSQL Death to Relational Databases(?), Scofield; Scalable SQL and NoSQL Data Stores, Cattel

22

Notional Big Data
Framework Taxonomy

Conceptual Structures:

Key Value Stores
Schema-less system
Column-oriented databases
Storage by column, not row

Value

Key
Name

Height

Eye Color

Bob

6’2”

Brown

Nancy

5’3”

Hazel

Relationships

Graph Databases
Uses nodes and edges to represent data
Often used for Semantic Web
Document Oriented Database
Stores documents that are semi-structured
Includes XML databases
Sharded RDBMS

Data

Data

Key
Key
RDBMS

Structured
Document
Structured
Document
RDBMS

RDBMS

23

Comparison of NoSQL and Relational Approaches
Performance

Horizontal
Scalability

Flexibility in Complexity
Functionality
Data Variety of Operation

Key-Value
stores

high

high

high

none

variable
(none)

Column
stores

high

high

moderate

low

minimal

Document
stores

high

variable (high)

high

low

variable (low)

Graph
databases

variable

variable

high

high

graph theory

Relational
databases

variable

variable

low

moderate

relational
algebra

Matches columns on the big data taxonomy
Credit: NoSQL Death to Relational Databases(?), Scofield (column headings modified from original data for clarity)

24

Notional Suitability of Big Data Frameworks
for types of Big Data Problems
Horizontal
Scalability

Flexibility in
Data Variety

Appropriate
Big Data Types

Key-Value
stores

high

high

1, 2, 3

Column stores

high

moderate

1 (partially), 2,
3 (partially)

Document
stores

variable (high)

high

1, 2 (likely),
3 (likely)

Graph
databases

variable

high

1, 2 (maybe),
3 (maybe)

Sharded
Database

variable (high)

low

2 (likely)

25

Section 3: Security Implications and Areas of
Research

NIST Information Technology Laboratory

Hypothesis: Big Data approaches will open up new
avenues of IT security metrology
• “Revolutions in science have often been
preceded by revolutions in measurement,” Sinan Aral, New York University

• Arthur Coviello, Chairman RSA
– “Security must adopt a big data view… The age of
big data has arrived in security management.”
– We must collect data throughout the enterprise,
not just logs
– We must provide context and perform real time
analysis

• There is precious little information on how to
do this
Credit: Data, data everywhere, Economist; http://searchcloudsecurity.techtarget.com/news/2240111123/Coviello-talks-about-building-a-trusted-cloud-resilient-security

27

Big Data is Moving into IT Security Products
• Several years ago, some security companies had an
epiphany:
– Traditional relational implementations were not always
keeping up with data demands

• A changed industry:
–
–
–
–

Some were able to stick with traditional relational approaches
Some partitioned their data and used multiple relational silos
Some quietly switched over to NoSQL approaches
Some adopted a hybrid approach, putting high value data in a
relational store and lower value data in NoSQL stores

Credit: This is based on my discussions with IT security companies in 12/2011 at the Government Technology Research Alliance Security Council 2011

Security Features are Slowly Moving into
Big Data Implementations
• Many big data systems were not designed with security
in mind – Tim Mather, KPMG
• There are far more security controls for relational
systems than for NoSQL systems
– SQL security: secure configuration management, multifactor
authentication, data classification, data encryption,
consolidated auditing/reporting, database firewalls,
vulnerability assessment scanners
– NoSQL security: cell-level access labels, kerberos-based
authentication, access control lists for tables/column families

Credit: Securing Big Data, Cloud Security Alliance Congress 2011, Tim Mather KPMG

29

Public Government Big Data
Security Research Exists
• Accumulo
– Accumulo is a distributed key/value store that provides
expressive, cell-level access labels.
– Allows fine grained access control in a NoSQL
implementation
– Based on Google BigTable
– 200,000 lines of Java code
– Submitted by the NSA to the Apache Foundation

Credit: http://wiki.apache.org/incubator/AccumuloProposal, http://www.informationweek.com/news/government/enterprise-apps/231600835

30

What further research needs to be conducted on big
data security and privacy?
• Enhancing IT security metrology
• Enabling secure implementations
• Privacy concerns on use of big
data technology

31

Research Area 1: The Computer
Science of Big Data
1. What is a definition of big data?
– What computer science properties are we trying to instantiate?

2. What types of big data frameworks exist?
– Can we identify a taxonomy that relates them hierarchically?

3. What are the strengths, weaknesses, and appropriateness of
big data frameworks for specific classes of problems?
– What are the mathematical foundations for big data frameworks?

4. How can we measure the consistency provided by a big data
solution?
5. Can we define standard for querying big data solutions?
With an understanding of the capabilities available and their
suitability for types of problems, we can then apply this
knowledge to computer security.

32

Research Area 2: Furthering IT Security
Metrology through Big Data Technology
1. Determine how IT security metrology is limited by traditional
data representations (i.e., highly structured relational
storage)
2. Investigate how big data frameworks can benefit IT security
measurement
• What new metrics could be available?
3. Identify specific security problems that can benefit from big
data approaches
• Conduct experiments to test solving identified problems
4. Explore the use of big data frameworks within existing
security products
• What new capabilities are available?
• How has this changed processing capacity?

33

Research Area 3: The Security of
Big Data Infrastructure
1. Evaluate the security capabilities of big data
infrastructure
– Do the available tools provide needed security features?
– What security models can be used when implementing big
data infrastructure?

2. Identify techniques to enhance security in big data
frameworks (e.g., data tagging approaches, sHadoop)
– Conduct experiments on enhanced security framework
implementations

34

Research Area 4: The Privacy of Big Data
Implementations
• Big data technology enables massive data aggregation
beyond what has been previously possible
• Inferencing concerns with non-sensitive data
• Legal foundations for privacy in data aggregation
• Application of NIST Special Publication 800-53 privacy
controls

35

Needed Research Deliverables
• Area 1 (not security specific)
– Publication on harnessing big data technology
• Definitions, taxonomies, and appropriateness for classes
of problems

• Area 2 (security specific)
– Publication on furthering IT security metrology through big
data technology
– Research papers on solving specific security problems using
big data approaches

• Area 3 (security specific)
– Publication on approaches for the secure use of big data
platforms

• Area 4 (privacy specific)
– Not yet identified
36

Section 4: MapReduce and Hadoop

NIST Information Technology Laboratory

MapReduce –
Dean, et al.
• Seminal paper published by Google in 2004
– Simple concurrent programming model and associated implementation

• Model handles the parallel processing and message passing
details
– Simplified coding model compared to general purpose parallel languages
(e.g., MPI)

• Three functions: Map -> Parallel sort -> Reduce
– Map: Processes a set of key/value pairs to produce an intermediate set of
key/value pairs
– Parallel sort: a distributed sort on intermediate results feeds the reduce
nodes
– Reduce: for each resultant key, it processes each key/value pair and
produces the result set of values for each key

• Approachable programming model
– Handles concurrency complexities for the user
– Limited functionality
– Appears to provide a sweet spot for solving a vast number of important
problems with an easy to use programming model
Credit: MapReduce: Simplified Data Processing on Large Clusters, Dean et al.

38

MapReduce Diagram from Google’s
2004 Seminal Paper

e

39

Storage, MapReduce, and Query
(SMAQ) Stacks

Query

• Efficient way of defining computation
• Platform for user friendly analytical
systems

Map
Reduce

• Distributes computation over many
servers
• Batch processing model

Storage

• Distributed and non-relational

Credit: 2011 O’Reilly Radar, Edd Dumbill

40

Hadoop
• Widely used MapReduce framework
• “The Apache Hadoop software library is a framework
that allows for the distributed processing of large data
sets across clusters of computers using a simple
programming model” – hadoop.apache.org
• Open source project with an ecosystem of products
• Core Hadoop:
– Hadoop MapReduce implementation
– Hadoop Distributed File System (HDFS)

• Non-core: Many related projects
Credit: http://hadoop.apache.org

41

Hadoop SMAQ Stack (select components)

Query

Map Reduce
Storage

•
•
•
•
•
•

Pig (simply query language)
Hive (SQL like queries)
Cascading (workflows)
Mahout (machine learning)
Zookeeper (coordination service)
Hama (scientific computation)

• Hadoop Map Reduce implementation
• HBase (column oriented database)
• Hadoop Distributed File System
(HDFS, core Hadoop file system)

42

Alternate MapReduce Frameworks
•
•
•
•
•
•

BashReduce
Disco Project
Spark
GraphLab Carnegie-Mellon
Storm
HPCC (LexisNexis)

43

Section 5: Notable Implementations
(both frameworks and infrastructure)

NIST Information Technology Laboratory

Google File System –
Ghemawat, et al.
• Design requirements: “performance, scalability,
reliability, and availability”
• Design assumptions:
–
–
–
–

Huge files
Expected component failures
File mutation is primarily by appending
Relaxed consistency <- think of the CAP theorem here

• Master has all its data in memory (consistent and
available!!)
• All reads and writes occur directly between client and
chunkservers
• For writes, control flow is decoupled from pipelined
data flow
Credit: The Google File System, Ghemawat et al.

45

Google File System Architecture

46

Google File System Write Control and Data Flow
• Master assigned a
primary replica
• Client pipelines data
through replicas
• Client contacts
primary to instantiate
the write

47

Big Table –
Chang, Dean, Ghemawat, et al.
• “Big table is a sparse, distributed, persistent multidimensional sorted map”
– It is a non-relational key-value store / column-oriented database

• Row keys- table data is stored in row order
– Model supports atomic row manipulation

• Tablets- subsets of tables (a range of the rows)
– Unit of distribution/load balancing

• Column families group column keys
– Access control done on column families

•
•
•
•
•

Each cell can contain multiple time stamped versions
Uses Google File System (GFS) for file storage
Distributed lock service- Chubby
Implementation- Lightly loaded master plus tablet servers
Internal Google tool

Credit: Bigtable: A Distributed Storage System for Structured Data, Chang et. al.

48

Big Table Storage Paradigm
Column family

Column key
Column family

Row key
Timestamp

49

Hbase
• Open source Apache project
– Modeled after the Big Table research paper
– Implemented on top of the Hadoop Distributed File System

• “Use HBase when you need random, realtime
read/write access to your Big Data… hosting of very
large tables -- billions of rows X millions of columns -atop clusters of commodity hardware. HBase is an
open-source, distributed, versioned, column-oriented
store modeled after Google's Bigtable”

Credit: http://hbase.apache.org

50

Dynamo –
DeCandia, Hastorun,… , Vogels

Werner
Vogels,
Amazon
CTO

• 2007 paper describing Amazon’s implementation that had
been in production use for one year
• Dynamo is a key-value eventually consistent database
designed for scalability and high availability
–
–
–
–

Key lookup only (no relational schema or hierarchical namespace)
No central point of failure (i.e., nodes are symmetric)
Handles nodes of differing capability (i.e., heterogeneity)
Offers incremental scalability (i.e., you can add one node at a
time)
– Writes rarely rejected, reads may return multiple versions

• Strong focus on SLA performance guarantees (for 99.9%
of operations)
Credit: Dynamo: Amazon’s Highly Available Key-value Store, DeCandia et al.

51

Dynamo Techniques and Advantages

52

Dynamo Configurability and
Production Implementation
• “the primary advantage of Dynamo is that it provides the
necessary knobs… to tune their instances” – Dynamo paper
• Users controls three variables:
– N, number of hosts on which to replicate data
– R, minimum number of hosts that must participate in a read
• low value could increase inconsistency
– W, minimum number of hosts that must participate in a write
• low value could decrease durability

• Amazon Dynamo infrastructure
–
–
–
–
–

Dial-in the desired requests per second (elasticity)
Data stored in solid state drives for low latency access
Automatic replication across availability zones
Zero administration burden or even features
Developers choose between eventually consistent and strongly
consistent reads
53

Apache Cassandra
• Described as a “big table model running on an Amazon
dynamo-like infrastructure”
• Distributed database management system
– No single point of failure
– Designed for use with commodity servers
– Key-value with column indexing system
• “Rows” are keys that are randomly partitioned among
servers
• Keys maps to multiple values
• Values from multiple keys may be grouped into “column
families”
• Each key’s values are stored together (like a row oriented
RDBMS and yet the data has a column orientation)
– Cassandra Query Language (SQL like querying)

• Initially developed by Facebook and then open sourced
Credit: http://cassandra.apache.org

54

Questions and Comments
Peter Mell
NIST
Senior Computer Scientist
301-975-5572
[email protected]
http://twitter.com/petermmell

NIST Information Technology Laboratory

Appendix A: Seminal Research Results

NIST Information Technology Laboratory

CAP Theorem –
Brewer, 2000
• CAP: Consistency, Availability, Partition-tolerance
• It is impossible to have all three CAP properties in an
asynchronous distributed read/write system
– Asynchronous nature is key (i.e., no clocks)
– Any two properties can be achieved

• Delayed-t consistency possible for partially synchronous
systems (i.e., independent timers)
– All three properties can be guaranteed if the requests are separated
by a defined period of lossless messaging.

• ‘most real world systems are forced to settle with returning
“most of the data most of the time.”’

• Take away: Distributed read/write systems are limited by
intersystem messaging and may have to relax their CAP
requirements
Credit: Brewer’s Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services, Gilbert and Lynch

57

BASE vs. ACID –
Pritchett 2008
• CAP: Consistency, Availability, Partition Tolerance
• For distributed systems, we must have partitioning
• ACID (atomicity, consistency, isolation, durability)
– Focuses on consistency, not availability
– Properties are transparently provided by the database

• BASE (basically available, soft state, eventually consistent)
– Focuses on availability, not consistency
– Requires independent analysis of each application to achieve
these properties (i.e., this is harder than ACID)
– Promotes availability by avoiding two phase commits over the
network
– Application code may provide idempotence to avoid 2 phase
commits
Credit: BASE: An Acid Alternative, Pritchett

58

Origin of SQL –
Codd 1970
• Invented by Edgar F. Codd, IBM Fellow
• “A Relational Model of Data for Large Shared Data
Banks” published in 1970
• Relational algebra provides a mathematical basis for
database query languages (i.e. SQL)
• Based on first order logic

• “[His] basic idea was that relationships between data
items should be based on the item's values, and not on
separately specified linking or nesting. This notion
greatly simplified the specification of queries and allowed
unprecedented flexibility to exploit existing data sets in
new ways“ - Don Chamberlin, SQL co-inventor
Credit: A Relational Model of Data for Large Shared Data Banks, Codd

59

The Notion of CoSQL –
Meijer, et al. 2011
• SQL and key-value NoSQL (called coSQL) are
mathematical duals based on category theory
• SQL and CoSQL can transmute into each other
• Duality between synchronous ACID and asynchronous
BASE
– Transmute data to take advantage of either ACID or BASE

• “monads and monad comprehensions provide a
common query mechanism for both SQL and coSQL”
• Microsoft’s Languge-Integrated Query (LINQ)
implements this query abstraction
• There could be a single language, analogous to SQL,
used for all coSQL databases!!
Credit: A co-Relational Model of Data for Large Shared Data Banks, Meijer

60

Notional Big Data Taxonomy w/
Mappings to Mathematical Systems
Big Data
Predicate
Logic
Big Data
Science
Relational
Algebra

Codd

Category
Theory

NoSQL

Meijer

SQL

ACID

Key Value

CAP Theorem
Brewer

BASE

Big Data
Platforms

Document
Stores

Pritchett

Graph
Databases

Column
Oriented

61

Appendix B: Overview of Big Data Framework
Types

NIST Information Technology Laboratory

Overview – Key Value Stores
• Key value stores are distributed associative arrays
(collections of key/value pairs)
– Implementing maps or dictionaries

• Enables storage of schema-less data
• Simple operations:
– add(key,value), set(key,value), get(key), delete(key)

• Values may be complex and unstructured objects
• Indexed on keys for searching
– No joins, no SQL, no real queries

• Often a speed advantage in storage and retrieval

Credit: Key-Value stores: a practical overview, Seeger

63

Example Uses- Key Value Stores
• Key value is one of the most prominent NoSQL
approaches
• Used for a wide variety of big data problems
• Most popular type is MapReduce
– First used for indexing the Internet (Google)

• Example:
– Visa used Hadoop to process 36 terabytes of data
– Traditional approach: one month
– Key value approach: 13 minutes

64

Overview – Column Oriented Databases
• Table data is stored by column NOT row
–
–
–
–

Efficient for calculating metrics on a particular set of columns
Efficient for updating all values in a single column
Inefficient for row operations effecting multiple columns
Inefficient for writing new rows

• Key concern is gaining efficiency in hard disk accesses
for particular types of jobs
• Since column data is of the same type, some
compression advantages can be achieved over roworiented databases
• This concept is not new. It has been used since the
1970s
Credit: C-Store: A Column-oriented DBMS, Stonebraker

65

Example – Column Oriented Databases
2010 Car

0-30 mph (sec) Turning circle Horsepower

Nissan Altima 2.5S 3.3

40 ft.

175

Mini Cooper
Clubman

3.8

37 ft.

118

Smart

5.1

30 ft.

71

Column oriented storage:
Nissan Altima 2.5S, Mini Cooper Clubman, Smart
3.3, 3.8, 5.1
40 ft., 37 ft., 30 ft.
175, 118, 72
Row oriented storage:
Nissan Altima 2.5S, 3.3, 40 ft., 175
Mini Cooper Clubman, 3.8, 37 ft., 118
Smart, 5.1, 30 ft., 71
Credit: Consumer Reports New Car Preview 2011

66

Overview - Graph Databases
• Implement the “Associative Model of Data”
– By definition: index free adjacency
– Not record based and no global index
– Nodes and edges

• Graphs, Directed Graphs, Multi-graphs, Hyper-graphs
• Compared to RDBMS, graph database:
–
–
–
–
–
–

Are slower for applying operations to large datasets
Have no join operations
Excellent for application of graph algorithms
Can be faster for associative data sets
Map well to object oriented structures
Can evolve to changing data types (there is no rigid schema)

• Useful for semantic web implementations (RDF)
Credit: http://www.w3schools.com/web/web_semantic.asp

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Example Use- Graph Databases for
Semantic Web
• The semantic web gives meaning to data by defining
relationships and properties
– Entities and relationships are described using RDF
• Subject -> Predicate -> Object
– Ontologies are defined using RDFS/OWL
• Enables inferences
• Full use of OWL can produce NP-complete computations

• RDF can be “naturally” represented using a labeled,
directed, multi-graph
• SPARQL enables RDF querying
• Relational databases can store RDF triples easily but
efficient SPARQL->SQL querying is difficult
• Native graph databases may provide enhanced
performance
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Overview - Document Oriented Database
• Focused on efficient management of semi-structured
data
– Focus on self-describing structures (e.g., YAML, JSON, PDF,
MS Office, or XML)

• Documents are like records in a RDBMS
• Each document
– may use a different schema
– may populate different fields (there are no ‘empty’ fields)

• Document may be accessed through
–
–
–
–

Unique keys
Metadata/tagging
Collections of documents / Directory structures
Query languages (varying by implementation)

Credit: NoSQL and Document Oriented Databases, Morin

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Example Use - Document Oriented
Databases for XML
• Native XML vs. XML-enabled
• Rationale: If XML is used for communication, why not store
the data natively in XML?
• Definition:
– Model based on XML document structure, not the data
– Each “record” is an XML document (analogous to a row in an
RDBMS)
– There are no requirements on the storage technique (e.g.,
relational vs. non-relational)

• Many provide “collections” of documents that may be
arranged hierarchically like a file system
• Querying Languages: Xpath, XQuery (extends Xpath)
• Transformations for output may use XSLT
• Hybrid databases have been developed that support
querying with SQL and XQuery
Credit: XML Database Products, Bourret; XML and Databases, Bourret

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